은행코드 조회
bank_codeList Korean bank codes and names. 대한민국 은행 코드·은행명 전체 목록을 반환합니다. transfer_1won, account_realname Tool의 bank_code 입력값을 찾을 때 사용합니다. 무료입니다. [무료]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
bank_codeList Korean bank codes and names. 대한민국 은행 코드·은행명 전체 목록을 반환합니다. transfer_1won, account_realname Tool의 bank_code 입력값을 찾을 때 사용합니다. 무료입니다. [무료]
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations already providing readOnlyHint and openWorldHint, the description adds '전체 목록을 반환합니다' (returns the full list), indicating no pagination or filtering, and states it is free ('무료입니다'). This goes beyond the annotations, though it doesn't elaborate on output format or potential response sizes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively short and front-loaded with the primary action. However, it contains redundancy: the Korean phrase repeats the English statement, and '무료입니다' appears twice (plain text and as '[무료]'). This is somewhat wasteful but not overly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, no-output-schema tool, the description covers purpose, usage context, and a key behavioral trait (full list). It also names dependent tools, which helps the agent understand when to invoke it. Minor gaps: no mention of response format or update frequency, but given the simplicity, it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and schema coverage is 100% vacuously. Per the baseline for 0 params, the description need not explain any parameter semantics. It could have further described output structure, but that's beyond parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists Korean bank codes and names ('List Korean bank codes and names'), using a specific verb and resource. It also provides the Korean equivalent, ensuring unambiguous purpose. The mention of usage with transfer_1won and account_realname further distinguishes its role among sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use this tool: 'transfer_1won, account_realname Tool의 bank_code 입력값을 찾을 때 사용합니다' (use when finding bank_code input values for transfer_1won and account_realname tools). This gives clear context and indirectly excludes other uses, though it doesn't name alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes; even within families like identi_card1-5 vs identi_card_image1-5, the text-input vs image-input distinction is clear. However, the sheer number of tools and some near-synonyms (e.g., ocr_identi1 vs identi_card_image1) could cause occasional misselection, but descriptions mitigate this.
Naming follows a loose verb-first pattern (check_, crawl_, download_, draw_, etc.) but includes significant deviations: bare nouns (bank_code, location, whois), numbered variants (identi_card1, identi_card_image1), and mixed prefixes (ocr_, identity_, etc.). The inconsistency is noticeable but still readable and predictable within functional clusters.
80 tools is far above the typical 3-15, but the server is a broad API aggregator covering many independent domains (banking, ID verification, media conversion, search, LLM, etc.), so the high count is somewhat justified. Still, the sheer number makes the toolkit feel unwieldy and hard to navigate, placing it at the high end of acceptable.
Within its stated purpose as a general-purpose utility API, the toolset covers a wide array of common task families: identity document verification (text and image), OCR field extraction, media conversion, web/search, domain/IP lookup, and LLM chat. Most operations have both get and act variants (e.g., set/get watermark, parcel_tracking/auto), with few obvious dead ends for typical use cases.